Wei Li

Orcid: 0000-0001-9822-6021

Affiliations:
  • Chinese University of Hong Kong, Department of Computer Science and Engineering, Hong Kong


According to our database1, Wei Li authored at least 14 papers between 2018 and 2022.

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Bibliography

2022
Adaptive Layout Decomposition With Graph Embedding Neural Networks.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

Rethinking Graph Neural Networks for the Graph Coloring Problem.
CoRR, 2022

2021
OpenMPL: An Open-Source Layout Decomposer.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021

Learning Point Clouds in EDA.
Proceedings of the ISPD '21: International Symposium on Physical Design, 2021

TreeNet: Deep Point Cloud Embedding for Routing Tree Construction.
Proceedings of the ASPDAC '21: 26th Asia and South Pacific Design Automation Conference, 2021

2020
Understanding Graphs in EDA: From Shallow to Deep Learning.
Proceedings of the ISPD 2020: International Symposium on Physical Design, Taipei, Taiwan, March 29, 2020

DeepBillboard: systematic physical-world testing of autonomous driving systems.
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020

Adaptive Layout Decomposition with Graph Embedding Neural Networks.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

2019
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization.
Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2019

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

FIT: Fill Insertion Considering Timing.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019

OpenMPL: An Open Source Layout Decomposer: Invited Paper.
Proceedings of the 13th IEEE International Conference on ASIC, 2019

2018
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems.
CoRR, 2018

A Unified Approximation Framework for Deep Neural Networks.
CoRR, 2018


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